As an illustration, consider that you are a smartphone operating systems provider and want to optimize for battery life based on phone usage.
Instead of investing in expensive hardware specialists and years of R&D,
on-device models driven by reinforcement learning algorithms such as the ones studied in this paper [1]}, [2]} can learn to optimize battery life in a personalized way from each individual phone's usage patterns.
However, unlike classical OR solvers which come with theoretical guarantees, our findings suggest that current learning-based approaches are not yet robust at handling situations beyond what they see in training and do not reliably scale to practical sizes.
Continuing our analogy, reinforcement learning models could eventually end up draining battery life faster if the architectures and policies are not robust or generalizable to, e.g. low-cost and obscure smartphones which are often used by marginalized or low-income groups [3]}.
